Hey Marketcalls – We just published a behind-the-scenes on scaling agentic SWE-bench evaluation and why throughput, isolation, and resumability can be the hidden constraint behind that curve. Link:
AGENTS
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AI21 Labs Shares Research on Scaling Agentic SWE-bench Evaluation
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Our Research team just dropped a few behind-the-scenes blogs on scaling agentic SWE-bench evaluation, including the failure modes we hit and what finally worked. I'm curious to hear your thoughts about our work
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Share insights on LLMs, AI Agents, and Machine Learning
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 9 janvier 2026
Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!https://t.co/NlfbhZZxKkIf you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Evaluation strategies for AI agents in real-world deployments
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New on the Anthropic Engineering Blog: Demystifying evals for AI agents. The capabilities that make agents useful also make them more difficult to evaluate. Here are evaluation strategies that have worked across real-world deployments.
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Snorkel Agentic Coding Benchmark: 100 Multi-Step Tasks
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100 multi-step tasks.
Multiple difficulty tiers.
Reproducible, sandboxed environments.
Human-validated reference solutions.
Meet the Snorkel Agentic Coding Benchmark. -
Agent Drift: The Hidden Failure Mode in Multi-Agent LLM Systems
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Few know of the agent drift problem in multi-agent systems. But it is one of the most common failure modes in multi-agent LLM systems. The more agents interact with each other, the worse they get. Not because individual models are weak. Because it's typical that something
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Chinese Robot Delivers 1000+ Popcorn Servings Per Movie Night
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China is living in the future with physical #AI:
— Amitav Bhattacharjee (@bamitav) 9 janvier 2026
A #robot neatly delivers 1,000+ servings of #popcorn per movie night in China's Shenzhen.@lexfridman @KirkDBorne @Ronald_vanLoon @erikbryn @antgrasso @sallyeaves @Nicochan33 @HaroldSinnott @mvollmer1 @marcusborba… pic.twitter.com/dmsVxRmYjaChina is living in the future with physical #AI: A #robot neatly delivers 1,000+ servings of #popcorn per movie night in China's Shenzhen. @lexfridman @KirkDBorne @Ronald_vanLoon @erikbryn @antgrasso @sallyeaves @Nicochan33 @HaroldSinnott @mvollmer1 @marcusborba
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Agentic AI at Edge Solves Hallucination Problem Manufacturing
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AI isn't going to run your factory… yet. But Agentic AI at the Edge is already solving the "Hallucination" problem. It’s all about context. https://
buff.ly/d9MS3Q6 #sponsored #highbyte_iiot #IIoT #EdgeAI #Manufacturing #Industry40 -
Building AI Agents Requires Complete Infrastructure Beyond Core Development
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Absolutely! Beyond just building the agent, you also need to manage memory, state, observability, tracing, logging, streaming, and more. It’s essentially a full infrastructure you have to design and maintain to ensure the agent actually does something useful.
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Agent Builders: From Building to Deployment Challenge
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Big moment for Agent builders!
— Akshay 🚀 (@akshay_pachaar) 9 janvier 2026
There's a pattern that keeps repeating in software.
First, everyone focuses on the "building" problem.
Frameworks emerge, mature, and become genuinely good. Then suddenly, the constraint flips to deployment.
We saw this with neural networks.… pic.twitter.com/9rLymQIJv0Big moment for Agent builders! There's a pattern that keeps repeating in software. First, everyone focuses on the "building" problem. Frameworks emerge, mature, and become genuinely good. Then suddenly, the constraint flips to deployment. We saw this with neural networks.